Senior Deep Learning and Computer Vision Engineer - Autonomous Vehicles

NVIDIA•Redmond, CA

10h

About The Position

NVIDIA GPUs are used broadly for Deep Learning, and NVIDIA is increasingly known as “the AI company”. Make your choice to join us today. We are training Deep Neural Networks for NVIDIA's Autonomous Vehicles effort, with the goal to enable autonomous driving. We are seeking Deep Learning/Machine Learning interns who are passionate about solving problems in perception, prediction, planning and control for self-driving cars to achieve full autonomy. Are you interested in inventing human level AI for navigation in the unconstrained world under any conditions? If so, join us! What you'll be doing: Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas. Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models. Taking approaches from initial evaluation and experimentation all the way to shipping. Defining and collecting training datasets. Building training pipelines and real-time inference run-times (PyTorch, TensorFlow, TensorRT, Python, C++).

Requirements

MS or PhD in Computer Science or equivalent experience.
5+ years of proven experience building robust software.
Passionate about Artificial Intelligence for robotics and autonomous navigation
Strive to learn new things and like solving hard problems
Math knowledge
Experience in Deep Learning / Machine Learning.
You have a background in Computer vision and/or Planning/Control.
Programming and debugging skills in C++ and/or Python.
Good communication and analytical skills.
Ability to work with multiple teams in a dynamic environment.

Nice To Haves

Background in applying latest AI methods to solve Computer Vision and Autonomous Vehicles problems
Experience with Unsupervised or Self-supervised Learning
Involvement with architecture optimization, pruning, curriculum & multi-task training
Experience fusing data from different sensor modalities (e.g. Images and LIDAR data) to enable information conflation, label propagation, cross training.

Responsibilities

Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas.
Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.
Taking approaches from initial evaluation and experimentation all the way to shipping.
Defining and collecting training datasets.
Building training pipelines and real-time inference run-times (PyTorch, TensorFlow, TensorRT, Python, C++)